Abstract

An inventory model for deteriorating item is considered in a random planning horizon under inflation and time value money. The model is described in two different environments: random and fuzzy random. The proposed model allows stock-dependent consumption rate and shortages with partial backlogging. In the fuzzy stochastic model, possibility chance constraints are used for defuzzification of imprecise expected total profit. Finally, genetic algorithm (GA) and fuzzy simulation-based genetic algorithm (FSGA) are used to make decisions for the above inventory models. The models are illustrated with some numerical data. Sensitivity analysis on expected profit function is also presented. Scope and Purpose. The traditional inventory model considers the ideal case in which depletion of inventory is caused by a constant demand rate. However, to keep sales higher, the inventory level would need to remain high. Of course, this would also result in higher holding or procurement cost. Also, in many real situations, during a longer-shortage period some of the customers may refuse the management. For instance, for fashionable commodities and high-tech products with short product life cycle, the willingness for a customer to wait for backlogging is diminishing with the length of the waiting time. Most of the classical inventory models did not take into account the effects of inflation and time value of money. But in the past, the economic situation of most of the countries has changed to such an extent due to large-scale inflation and consequent sharp decline in the purchasing power of money. So, it has not been possible to ignore the effects of inflation and time value of money any more. The purpose of this paper is to maximize the expected profit in the random planning horizon.

1. Introduction

In the past few decades, many researches have studied an inventory model with constant demand or dynamic demand (cf. M. K. Maiti and M. Maiti [1], Taleizadeh et al. [2], Jana et al. [3], and others). Moreover, in a competitive situation attractive display of units in the showroom is an important factor. Levin et al. [4] noted that at times the presence of inventory has a motivational effect on the people around it. It is a common belief that large piles of goods displayed in a supermarket will lead the customer to buy more. Thus, many business people use showrooms and the attractive display of units in the showroom to influence the customers. Roy et al. [5] and Maiti [6] have developed an inventory model with stock-dependent demand.

In most of the earlier inventory models, lifetime of an item is assumed to be infinite while it is in storage. But in reality, many physical goods deteriorate due to dryness, spoilage, vaporization, and so forth and are damaged due to hoarding longer than their normal storage period. The deterioration also depends on preserving facilities and environmental conditions in warehouses/storage. So, due to deterioration effect, a certain fraction of the items either damaged or decayed are not in perfect condition to satisfy the future demand of customers for good items. Deterioration for such items is continuous and constant or time-dependent and/or dependent on the on-hand inventory. A number of research papers have already been published on the above type of items by Roy et al. [5] and others.

Moreover, the effects of inflation and time value of money are vital in practical situation, especially in the developing countries with large-scale inflation. Therefore, the effect of inflation and time value of money cannot be ignored in real situations. To relax the assumption of non inflationary effects on costs, Buzacott [7] and Misra [8] simultaneously developed an EOQ model with a constant inflation rate for all associated costs. Bierman and Thomas [9] then proposed an EOQ model under inflation that also incorporated the discount rate. Misra [10] then extended the EOQ model with different inflation rates for various associated costs. Recently, Chern et al. [11] proposed partial backlogging inventory lot-size models for deteriorating items with fluctuating demand under inflation. Maity and Maiti [12] have developed a multiobjective optimal inventory control problem for deteriorating multi-items under fuzzy inflation and discounting. Yang et al. [13] proposed an inventory model under inflation for deteriorating items with stock-dependent consumption rate and partial backlogging shortages.

Use of GA in complex decision making problem is well established Michalewicz [14]. A simple GA starts with a set of potential solutions (called initial population) of the decision making problem under consideration. Individual solutions are called chromosome. Crossover and mutation operations happen among the potential solutions with some probability and respectively, to get a new set of solutions and it continues until terminating conditions are encountered. Behavior and performance of a GA is directly affected by the interaction between the parameters, that is, selection process of chromosomes for mating pool, () and so forth. Poor parameter settings usually lead to several problems such as premature convergence. Extensive research work has been made to improve the performance of GA for single/multiobjective continuous/discrete optimization problems during the last two decades. Michalewicz [14] proposed a genetic algorithm, named contractive mapping genetic algorithm (CMGA), where movement from old population to new population takes place only when average fitness of new population is better than the old one and proved the asymptotic convergence of the algorithm by Banach fixed-point theorem. Bessaou and Siarry [15] proposed a GA where initially more than one population of solutions are generated. Genetic operations are done on every population a finite number of times to find a promising zone of optimum solution. Finally, a population of solutions is generated in this zone and genetic operations are done on this population a finite number of times to get a final solution. Last and Eyal [16] developed a GA with varying population size, where chromosomes are classified into young, middle-aged, and old according to their age and lifetime. Genotype diversity and phenotype diversity of the final population are obtained to measure the performance of the GA. Pezzella et al. [17] developed a GA for the flexible Job-shop scheduling problem, which integrates different strategies for generating the initial population, selecting the individuals for reproduction and reproducing new individuals. In this research paper, an EPQ model of an item is developed in a random planning horizon; that is, lifetime of the product is assumed as random in nature and it follows an exponential distribution with known mean. Unit production cost decreases in each production cycle due to learning effects of the workers on production. Similarly setup cost in each cycle is partly constant and partly decreases in each cycle due to learning effects of the employees. Model is formulated to maximize the expected profit from the whole planning horizon. Following Last and Eyal [16], a GA with varying population size is implemented where chromosomes are classified into young, middle-aged and old according to their age and lifetime. In this GA, crossover probability is a function of parents' age type (young, middle-aged, old, etc.) and is obtained using a fuzzy rule base and fuzzy possibility theory Dubois and Prade [18]. It is an improved GA where a subset of better children is included with the parent population for next generation, and size of this subset is a percentage of the size of its parent set. This GA is used to make optimal decision for the above production inventory model. Performance of the proposed GA for solving the model is compared with that of basic GA and CMGA. The model is illustrated with some numerical data. Sensitivity analysis on expected profit function is also presented. Recently, many papers have been developed in GA (cf. Narmatha Banu and Devaraj [19], Kar et al. [20]).

Due to fuzzy inflation rate, the objective function is fuzzy in nature, and then following Liu and Iwamura [21], M. K. Maiti and M. Maiti [1], the said objective function is converted to a crisp objective function. Some research papers have been already published considering imprecise planning horizon (cf. M. K. Maiti and M. Maiti [1], Roy et al. [5, 22], and others). Till now, none has developed inventory models with both random planning horizon and imprecise effect due to inflation and discounting and stock-dependent demand. In this paper, a partial backlogging inventory model with stock-dependent demand for deterioration item has been developed under imprecise inflation rate over a random planning horizon. Here, the planning horizon is stochastic in nature and follows exponential distribution. The expected profit is maximized using a FSGA with roulette wheel selection, arithmetic crossover, and random mutation (Michalewicz, [14]). The models are illustrated with some numerical data. Sensitivity analysis on expected profit is presented.

2. Assumptions and Notations

2.1. Assumptions

The mathematical model of the inventory replenishment problem is based on the following assumptions. Demand rate is assumed to depend on the existing stock level. The time horizon (a random variable) is finite. The time horizon completely accommodates first cycles and ends during th cycle. Lead time is negligible. Replenishment rate is infinite but replenishment size is finite. Shortages are allowed. Unsatisfied demand is partially backlogged. The fraction of shortages backordered is a differentiable and decreasing function of time , denoted by , where is the waiting time up to the next replenishment, and with . Note that if (or 0) for all , then shortages are completely backlogged (or lost). For deteriorating items, a constant fraction of the on-hand inventory deteriorates per unit of time and there is no repair or replacement of the deteriorated inventory during the planning period.

2.2. Notations

For convenience, the following notations are used throughout the entire paper. : on-hand inventory of a cycle at time , .: the demand rate, where : number of fully accommodated cycles to be made during the prescribed time horizon. : duration of a complete cycle. : total ordered quantity in th cycle. : selling price per unit. : is setup cost in th cycle, ; ; . Here , , and are so chosen to best fit the setup cost function. It is also noted that, set-up cost decreases with the number of cycle due to the learning effect.: the external variable purchasing cost per unit. : the inventory holding cost per unit per unit time. : the backlogging cost per unit per unit time. : the deterioration rate per unit per unit time. : the discount rate. : the inflation rate, which is varied by the social economical situations. is the discount rate minus the inflation rate.: the backlogging rate which is a decreasing function of the waiting time , we here assume that , where , and is the waiting time. : holding cost in the th cycle. : holding cost in the last cycle. : sales revenue in the th cycle. : sales revenue in the last cycle. : purchasing cost in the th cycle. : purchasing cost in the last cycle. : total profit after completing fully accommodated cycles. : total profit for the last cycles. : expected total profit after completing fully accommodated cycles. : expected total profit from the last cycle. : expected total profit from the planning horizon. : total time horizon (a random variable) and is the finite time horizon. Here, it is assumed that the planning horizon is a random variable and follows exponential distribution with probability density function (p.d.f) as follows:  Here, is the parameter of the distribution. : the amount of backorders at time . : order quantity at time .

3. Mathematical Formulation

In the development of the model, we assume that there are full cycles during the random time horizon and the planning horizon ends within th cycle, that is, within and . The th replenishment is made at time . The quantity received at is used partly to meet the accumulated backorders in the previous cycle. The inventory at gradually reduces to zero at (cf. Figure 1). For the last cycle, some amount may be left after the end of planning horizon. This amount is sold at a reduced price in a lot.

Figure 1 

Graphical representation of inventory model.

3.1. Formulation for th Cycle

The differential equation describing the inventory level in the interval is given by where and .

Subject to the conditions that at .

The solution of the differential equation (3) is given by During the time interval , the demand rate and backlogged rate . Hence, the amount of backorders is governed by the following differential equation: subject to the conditions that at . The solutions of the differential equation (5) is given by From (4) and (6), we have the order quantity at in the th replenishment cycle: Present value of holding cost of the inventory for the th cycle is given by Present value of ordering cost for the th cycle is given by Present value of purchasing cost for the th cycle is given by Present value of sales revenue for the th cycle is given by Then, the present value of the total profit from the full cycle is as follows:

Now, So, Since the planning horizon has a p.d.f , the present value of expected total profit from complete cycles is given by

3.2. Formulation for Last Cycle

The differential equations describing the inventory level in the interval are given by where , subject to the conditions that The solutions of the differential equations (15) and (16) are given by From (17), we have the order quantity at in the th replenishment cycle: Present value of ordering cost is given by Present value of purchasing cost is given by In last cycle, for simplicity we consider two cases only depending upon the cycle length. Let be the real value corresponding to the random variable .

Case 1 (). Present value of holding cost of the inventory for the last cycle (cf. Figure 2) is given by Present value of sales revenue is given by

Figure 2 

Graphical representation of inventory model for Case 1.

Case 2 (). Present value of holding cost of the inventory for the last cycle (Figure 3) is given by Present value of sales revenue is given by So, expected holding cost for the last cycle is given by Expected sales revenue from the last cycle is given by Expected reduced selling price from the last cycle is given by Expected setup cost from the last cycle is given by So, expected total profit from last cycle is given by